General sets (LazySet)

LazySet{N}

Abstract type for the set types in LazySets.

Notes

LazySet types should be parameterized with a type N, typically N<:Real, for using different numeric types.

Every concrete LazySet must define the following method:

• dim(S::LazySet) – the ambient dimension of S

While not strictly required, it is useful to define the following method:

• σ(d::AbstractVector, S::LazySet) – the support vector of S in a given direction d

The method

• ρ(d::AbstractVector, S::LazySet) – the support function of S in a given direction d

is optional because there is a fallback implementation relying on σ. However, for potentially unbounded sets (which includes most lazy set types) this fallback cannot be used and an explicit method must be implemented.

The subtypes of LazySet (including abstract interfaces):

julia> subtypes(LazySet, false)
17-element Vector{Any}:
 AbstractAffineMap
 AbstractPolynomialZonotope
 Bloating
 CachedMinkowskiSumArray
 CartesianProduct
 CartesianProductArray
 Complement
 ConvexSet
 Intersection
 IntersectionArray
 MinkowskiSum
 MinkowskiSumArray
 Polygon
 QuadraticMap
 Rectification
 UnionSet
 UnionSetArray

If we only consider concrete subtypes, then:

julia> concrete_subtypes = subtypes(LazySet, true);

julia> length(concrete_subtypes)
54

julia> println.(concrete_subtypes);
AffineMap
Ball1
Ball2
BallInf
Ballp
Bloating
CachedMinkowskiSumArray
CartesianProduct
CartesianProductArray
Complement
ConvexHull
ConvexHullArray
DensePolynomialZonotope
Ellipsoid
EmptySet
ExponentialMap
ExponentialProjectionMap
HParallelotope
HPolygon
HPolygonOpt
HPolyhedron
HPolytope
HalfSpace
Hyperplane
Hyperrectangle
Intersection
IntersectionArray
Interval
InverseLinearMap
Line
Line2D
LineSegment
LinearMap
MinkowskiSum
MinkowskiSumArray
Polygon
QuadraticMap
Rectification
ResetMap
RotatedHyperrectangle
SimpleSparsePolynomialZonotope
Singleton
SparsePolynomialZonotope
Star
SymmetricIntervalHull
Tetrahedron
Translation
UnionSet
UnionSetArray
Universe
VPolygon
VPolytope
ZeroSet
Zonotope

plot_lazyset(X::LazySet{N}, [ε]::Real=N(PLOT_PRECISION); ...) where {N}

Plot a set.

Input

• X – set
• ε – (optional, default: PLOT_PRECISION) approximation error bound

Notes

This recipe just defines the default plotting options and then calls the function plot_recipe, which then implements the set-specific plotting.

The argument ε is ignored by some set types, e.g., for polyhedra (subtypes of AbstractPolyhedron).

Examples

julia> B = Ball2(ones(2), 0.1);

julia> plot(B, 1e-3)  # default accuracy value (explicitly given for clarity here)

julia> plot(B, 1e-2)  # faster but less accurate than the previous call

plot_list(list::AbstractVector{VN}, [ε]::Real=N(PLOT_PRECISION),
          [Nφ]::Int=PLOT_POLAR_DIRECTIONS; [same_recipe]=false; ...)
    where {N, VN<:LazySet{N}}

Plot a list of sets.

Input

• list – list of sets (1D or 2D)
• ε – (optional, default: PLOT_PRECISION) approximation error bound
• Nφ – (optional, default: PLOT_POLAR_DIRECTIONS) number of polar directions (used to plot lazy intersections)
• same_recipe – (optional, default: false) switch for faster plotting but without individual plot recipes (see notes below)

Notes

For each set in the list we apply an individual plot recipe.

The option same_recipe provides access to a faster plotting scheme where all sets in the list are first converted to polytopes and then plotted in one single run. This, however, is not suitable when plotting flat sets (line segments, singletons) because then the polytope plot recipe does not deliver good results. Hence by default we do not use this option. For plotting a large number of (non-flat) polytopes, we highly advise activating this option.

Examples

julia> B1 = BallInf(zeros(2), 0.4);

julia> B2 = BallInf(ones(2), 0.4);

julia> plot([B1, B2])

Some of the sets in the list may not be plotted precisely but rather overapproximated first. The second argument ε controls the accuracy of this overapproximation.

julia> Bs = [BallInf(zeros(2), 0.4), Ball2(ones(2), 0.4)];

julia> plot(Bs, 1e-3)  # default accuracy value (explicitly given for clarity)

julia> plot(Bs, 1e-2)  # faster but less accurate than the previous call

plot_vlist(X::S, ε::Real) where {S<:LazySet}

Return a list of vertices used for plotting a two-dimensional set.

Input

• X – two-dimensional set
• ε – precision parameter

Output

A list of vertices of a polygon P. For convex X, P usually satisfies that the Hausdorff distance to X is less than ε.

plot3d(S::LazySet; [backend]=default_polyhedra_backend(S), [alpha]=1.0,
       [color]=:blue, [colormap]=:viridis, [colorrange]=nothing,
       [interpolate]=false, [linewidth]=1, [overdraw]=false, [shading]=true,
       [transparency]=true, [visible]=true)

Plot a three-dimensional set using Makie.

Input

• S – set
• backend – (optional, default: default_polyhedra_backend(S)) backend for polyhedral computations
• alpha – (optional, default: 1.0) float in [0,1]; the alpha or transparency value
• color – (optional, default: :blue) Symbol or Colorant; the color of the main plot element (markers, lines, etc.), which can be a color symbol/string like :red
• colormap – (optional, default: :viridis) the color map of the main plot; use available_gradients() to see which gradients are available, which can also be used as [:red, :black]
• colorrange – (optional, default: nothing, which falls back to Makie.Automatic()) a tuple (min, max) where min and max specify the data range to be used for indexing the colormap
• interpolate – (optional, default: false) a boolean for heatmap and images; toggles color interpolation between nearby pixels
• linewidth – (optional, default: 1) a number that specifies the width of the line in line and linesegments plots
• overdraw – (optional, default: false)
• shading – (optional, default: true) a boolean that toggles shading (for meshes)
• transparency – (optional, default: true) if true, the set is transparent, otherwise it is displayed as a solid object
• visible – (optional, default: true) a boolean that toggles visibility of the plot

For a complete list of attributes and usage see Makie's documentation.

Notes

This plot recipe works by computing the list of constraints of S and converting to a polytope in H-representation. Then, this polytope is transformed with Polyhedra.Mesh and plotted using the mesh function.

If the function constraints_list is not applicable to your set S, try overapproximation first; e.g. via

julia> Sapprox = overapproximate(S, SphericalDirections(10))

julia> using Polyhedra, GLMakie

julia> plot3d(Sapprox)

The number 10 above corresponds to the number of directions considered; for better resolution use higher values (but it will take longer).

For efficiency consider using the CDDLib backend, as in

julia> using CDDLib

julia> plot3d(Sapprox, backend=CDDLib.Library())

Examples

The functionality requires both Polyhedra and a Makie backend. After loading LazySets, do using Polyhedra, GLMakie (or another Makie backend).

julia> using LazySets, Polyhedra, GLMakie

julia> plot3d(10 * rand(Hyperrectangle, dim=3))

julia> plot3d!(10 * rand(Hyperrectangle, dim=3), color=:red)

plot3d!(S::LazySet; backend=default_polyhedra_backend(S), [alpha]=1.0,
       [color]=:blue, [colormap]=:viridis, [colorrange]=nothing,
       [interpolate]=false, [linewidth]=1, [overdraw]=false, [shading]=true,
       [transparency]=true, [visible]=true)

Plot a three-dimensional set using Makie.

Input

See plot3d for the description of the inputs. For a complete list of attributes and usage see Makie's documentation.

Notes

See the documentation of plot3d for examples.

○(c, a)

Convenience constructor of Ellipsoids or Ball2s depending on the type of a.

Input

• c – center
• a – additional parameter (either a shape matrix for Ellipsoid or a radius for Ball2)

Output

A Ellipsoids or Ball2s depending on the type of a.

Notes

The function symbol can be typed via \bigcirc[TAB].

isconvextype(X::Type{<:LazySet})

Check whether the given LazySet type is convex.

Input

• X – subtype of LazySet

Output

true if the given set type is guaranteed to be convex by using only type information, and false otherwise.

Notes

Since this operation only acts on types (not on values), it can return false negatives, i.e., there may be instances where the set is convex, even though the answer of this function is false. The examples below illustrate this point.

Examples

A ball in the infinity norm is always convex, hence we get:

julia> isconvextype(BallInf)
true

For instance, the union (UnionSet) of two sets may in general be either convex or not. Since convexity cannot be decided by just using type information, isconvextype returns false.

julia> isconvextype(UnionSet)
false

However, the type parameters of set operations allow to decide convexity in some cases by falling back to the convexity information of the type of its arguments. Consider for instance the lazy intersection. The intersection of two convex sets is always convex, hence we get:

julia> isconvextype(Intersection{Float64, BallInf{Float64}, BallInf{Float64}})
true

low(X::LazySet)

Return a vector with the lowest coordinates of the set in each canonical direction.

Input

• X – set

Output

A vector with the lower coordinate of the set in each dimension.

Notes

See also low(X::LazySet, i::Int).

The result is the lowermost corner of the box approximation, so it is not necessarily contained in X.

high(X::LazySet)

Return a vector with the highest coordinate of the set in each canonical direction.

Input

• X – set

Output

A vector with the highest coordinate of the set in each dimension.

Notes

See also high(X::LazySet, i::Int).

The result is the uppermost corner of the box approximation, so it is not necessarily contained in X.

extrema(X::LazySet, i::Int)

Return the lower and higher coordinate of a set in a given dimension.

Input

• X – set
• i – dimension of interest

Output

The lower and higher coordinate of the set in the given dimension.

Notes

The result is equivalent to (low(X, i), high(X, i)), but sometimes it can be computed more efficiently.

Algorithm

The bounds are computed with low and high.

extrema(X::LazySet)

Return two vectors with the lowest and highest coordinate of X in each canonical direction.

Input

• X – set

Output

Two vectors with the lowest and highest coordinates of X in each dimension.

Notes

See also extrema(X::LazySet, i::Int).

The result is equivalent to (low(X), high(X)), but sometimes it can be computed more efficiently.

The resulting points are the lowermost and uppermost corners of the box approximation, so they are not necessarily contained in X.

Algorithm

The bounds are computed with low and high by default.

convex_hull(X::LazySet; kwargs...)

Compute the convex hull of a polytopic set.

Input

• X – polytopic set

Output

The set X itself if its type indicates that it is convex, or a new set with the list of the vertices describing the convex hull.

Algorithm

For non-convex sets, this method relies on the vertices_list method.

triangulate(X::LazySet)

Triangulate a three-dimensional polyhedral set.

Input

• X – three-dimensional polyhedral set

Output

A tuple (p, c) where p is a matrix, with each column containing a point, and c is a list of 3-tuples containing the indices of the points in each triangle.

basetype(T::Type{<:LazySet})

Return the base type of the given set type (i.e., without type parameters).

Input

• T – set type

Output

The base type of T.

basetype(S::LazySet)

Return the base type of the given set (i.e., without type parameters).

Input

• S – set

Output

The base type of S.

Examples

julia> Z = rand(Zonotope);

julia> basetype(Z)
Zonotope

julia> basetype(Z + Z)
MinkowskiSum

julia> basetype(LinearMap(rand(2, 2), Z + Z))
LinearMap

isboundedtype(T::Type{<:LazySet})

Check whether a set type only represents bounded sets.

Input

• T – set type

Output

true if the set type only represents bounded sets. Note that some sets may still represent an unbounded set even though their type actually does not (example: HPolytope, because the construction with non-bounding linear constraints is allowed).

Notes

By default this function returns false. All set types that can determine boundedness should override this behavior.

isbounded(S::LazySet)

Check whether a set is bounded.

Input

• S – set
• algorithm – (optional, default: "support_function") algorithm choice, possible options are "support_function" and "stiemke"

Output

true iff the set is bounded.

Algorithm

See the documentation of _isbounded_unit_dimensions or _isbounded_stiemke for details.

_isbounded_unit_dimensions(S::LazySet)

Check whether a set is bounded in each unit dimension.

Input

• S – set

Output

true iff the set is bounded in each unit dimension.

Algorithm

This function asks for upper and lower bounds in each ambient dimension.

is_polyhedral(S::LazySet)

Trait for polyhedral sets.

Input

• S – set

Output

true only if the set behaves like an AbstractPolyhedron.

Notes

The answer is conservative, i.e., may sometimes be false even if the set is polyhedral.

isfeasible(constraints::AbstractVector{<:HalfSpace}, [witness]::Bool=false;
           [solver]=nothing)

Check for feasibility of a list of linear constraints.

Input

• constraints – list of linear constraints
• witness – (optional; default: false) flag for witness production
• solver – (optional; default: nothing) LP solver

Output

true if the linear constraints are feasible, and false otherwise.

Algorithm

This implementation solves the corresponding feasibility linear program.

norm(S::LazySet, [p]::Real=Inf)

Return the norm of a set. It is the norm of the enclosing ball (of the given $p$-norm) of minimal volume that is centered in the origin.

Input

• S – set
• p – (optional, default: Inf) norm

Output

A real number representing the norm.

radius(S::LazySet, [p]::Real=Inf)

Return the radius of a set. It is the radius of the enclosing ball (of the given $p$-norm) of minimal volume with the same center.

Input

• S – set
• p – (optional, default: Inf) norm

Output

A real number representing the radius.

diameter(S::LazySet, [p]::Real=Inf)

Return the diameter of a set. It is the maximum distance between any two elements of the set, or, equivalently, the diameter of the enclosing ball (of the given $p$-norm) of minimal volume with the same center.

Input

• S – set
• p – (optional, default: Inf) norm

Output

A real number representing the diameter.

isempty(P::LazySet{N}, witness::Bool=false;
        [use_polyhedra_interface]::Bool=false, [solver]=nothing,
        [backend]=nothing) where {N}

Check whether a polyhedral set is empty.

Input

• P – polyhedral set
• witness – (optional, default: false) compute a witness if activated
• use_polyhedra_interface – (optional, default: false) if true, we use the Polyhedra interface for the emptiness test
• solver – (optional, default: nothing) LP-solver backend; uses default_lp_solver(N) if not provided
• backend – (optional, default: nothing) backend for polyhedral computations in Polyhedra; uses default_polyhedra_backend(P) if not provided

Output

• If witness option is deactivated: true iff $P = ∅$
• If witness option is activated:
  • (true, []) iff $P = ∅$
  • (false, v) iff $P ≠ ∅$ and $v ∈ P$

Notes

The default value of the backend is set internally and depends on whether the use_polyhedra_interface option is set or not. If the option is set, we use default_polyhedra_backend(P).

Witness production is not supported if use_polyhedra_interface is true.

Algorithm

The algorithm sets up a feasibility LP for the constraints of P. If use_polyhedra_interface is true, we call Polyhedra.isempty. Otherwise, we set up the LP internally.

linear_map(M::AbstractMatrix, P::LazySet; kwargs...)

Concrete linear map of a polyhedral set.

Input

• M – matrix
• P – polyhedral set

Output

A set representing the concrete linear map.

linear_map(a::Number, X::LazySet; kwargs...)

Alias for scale(a, X; kwargs...).

affine_map(M, X::LazySet, v::AbstractVector; kwargs...)

Compute the concrete affine map $M·X + v$.

Input

• M – linear map
• X – set
• v – translation vector

Output

A set representing the affine map $M·X + v$.

Algorithm

The implementation applies the functions linear_map and translate.

exponential_map(M::AbstractMatrix, X::LazySet)

Compute the concrete exponential map of M and X, i.e., exp(M) * X.

Input

• M – matrix
• X – set

Output

A set representing the exponential map of M and X.

Algorithm

The implementation applies the functions exp and linear_map.

an_element(S::LazySet)

Return some element of a set.

Input

• S – set

Output

An element of a set.

Algorithm

An element of the set is obtained by evaluating its support vector along direction $[1, 0, …, 0]$. This may fail for unbounded sets.

tosimplehrep(S::LazySet)

Return the simple constraint representation $Ax ≤ b$ of a polyhedral set from its list of linear constraints.

Input

• S – polyhedral set

Output

The tuple (A, b) where A is the matrix of normal directions and b is the vector of offsets.

Algorithm

This fallback implementation relies on constraints_list(S).

reflect(P::LazySet)

Concrete reflection of a set P, resulting in the reflected set -P.

Input

• P – polyhedral set

Output

The set -P, which is either of type HPolytope if P is a polytope (i.e., bounded) or of type HPolyhedron otherwise.

Algorithm

This function requires that the list of constraints of the set P is available, i.e., that it can be written as $P = \{z ∈ ℝⁿ: ⋂ sᵢᵀz ≤ rᵢ, i = 1, ..., N\}.$

This function can be used to implement the alternative definition of the Minkowski Difference

\[ A ⊖ B = \{a − b \mid a ∈ A, b ∈ B\} = A ⊕ (-B)\]

by calling minkowski_sum(A, reflect(B)).

is_interior_point(d::AbstractVector{N}, X::LazySet{N};
                  p=N(Inf), ε=_rtol(N)) where {N}

Check whether the point d is contained in the interior of the set X.

Input

• d – point
• X – set
• p – (optional; default: N(Inf)) norm of the ball used to apply the error tolerance
• ε – (optional; default: _rtol(N)) error tolerance of check

Output

Boolean which indicates if the point d is contained in X.

Algorithm

The implementation checks if a Ballp of norm p with center d and radius ε is contained in the set X. This is a numerical check for d ∈ interior(X) with error tolerance ε.

isoperationtype(X::Type{<:LazySet})

Check whether the given set type is an operation or not.

Input

• X – set type

Output

true if the given set type is a set operation and false otherwise.

Notes

This fallback implementation returns an error that isoperationtype is not implemented. Subtypes of LazySet should dispatch on this function as required.

See also isoperation(X<:LazySet).

Examples

julia> isoperationtype(BallInf)
false

julia> isoperationtype(LinearMap)
true

isoperation(X::LazySet)

Check whether a set is an instance of a set operation or not.

Input

• X – set

Output

true if X is an instance of a set-based operation and false otherwise.

Notes

This fallback implementation checks whether the set type of the input is an operation type using isoperationtype(::Type{<:LazySet}).

Examples

julia> B = BallInf([0.0, 0.0], 1.0);

julia> isoperation(B)
false

julia> isoperation(B ⊕ B)
true

isequivalent(X::LazySet, Y::LazySet)

Check whether two sets are equal in the mathematical sense, i.e., equivalent.

Input

• X – set
• Y – set

Output

true iff X is equivalent to Y (up to some precision).

Algorithm

First we check X ≈ Y, which returns true if and only if X and Y have the same type and approximately the same values (checked with LazySets._isapprox). If that fails, we check the double inclusion X ⊆ Y && Y ⊆ X.

Examples

julia> X = BallInf([0.1, 0.2], 0.3);

julia> Y = convert(HPolytope, X);

julia> X == Y
false

julia> isequivalent(X, Y)
true

surface(X::LazySet)

Compute the surface area of a set.

Input

• X – set

Output

A real number representing the surface area of X.

area(X::LazySet)

Compute the area of a two-dimensional polytopic set.

Input

• X – two-dimensional polytopic set

Output

A number representing the area of X.

Notes

This algorithm is applicable to any polytopic set X whose list of vertices can be computed via vertices_list.

Algorithm

Let m be the number of vertices of X. We consider the following instances:

• m = 0, 1, 2: the output is zero.
• m = 3: the triangle case is solved using the Shoelace formula with 3 points.
• m = 4: the quadrilateral case is solved by the factored version of the Shoelace formula with 4 points.

Otherwise, the general Shoelace formula is used; for details see the Wikipedia page.

concretize(X::LazySet)

Construct a concrete representation of a (possibly lazy) set.

Input

• X – set

Output

A concrete representation of X (as far as possible).

Notes

Since not every lazy set has a concrete set representation in this library, the result may be partially lazy.

complement(X::LazySet)

Return the complement of a polyhedral set.

Input

• X – polyhedral set

Output

A UnionSetArray of half-spaces, i.e., the output is the union of the linear constraints which are obtained by complementing each constraint of X.

Algorithm

The principle used in this implementation is that for any pair of sets $(X, Y)$ we have that $(X ∩ Y)^C = X^C ∪ Y^C$. In particular, we can apply this rule for each constraint that defines a polyhedral set. Hence the concrete complement can be represented as the set union of the complement of each constraint.

polyhedron(P::LazySet; [backend]=default_polyhedra_backend(P))

Compute a set representation from Polyhedra.jl.

Input

• P – polyhedral set
• backend – (optional, default: call default_polyhedra_backend(P)) the polyhedral computations backend

Output

A set representation in the Polyhedra library.

Notes

For further information on the supported backends see Polyhedra's documentation.

Algorithm

This default implementation uses tosimplehrep, which computes the constraint representation of P. Set types preferring the vertex representation should implement their own method.

project(S::LazySet, block::AbstractVector{Int}, [::Nothing=nothing],
        [n]::Int=dim(S); [kwargs...])

Project a set to a given block by using a concrete linear map.

Input

• S – set
• block – block structure - a vector with the dimensions of interest
• nothing – (default: nothing)
• n – (optional, default: dim(S)) ambient dimension of the set S

Output

A set representing the projection of the set S to block block.

Algorithm

We apply the function linear_map.

project(S::LazySet, block::AbstractVector{Int}, set_type::Type{TS},
        [n]::Int=dim(S); [kwargs...]) where {TS<:LazySet}

Project a set to a given block and set type, possibly involving an overapproximation.

Input

• S – set
• block – block structure - a vector with the dimensions of interest
• set_type – target set type
• n – (optional, default: dim(S)) ambient dimension of the set S

Output

A set of type set_type representing an overapproximation of the projection of S.

Algorithm

1. Project the set S with M⋅S, where M is the identity matrix in the block

coordinates and zero otherwise.

1. Overapproximate the projected set using overapproximate and set_type.

project(S::LazySet, block::AbstractVector{Int},
        set_type_and_precision::Pair{T, N}, [n]::Int=dim(S);
        [kwargs...]) where {T<:UnionAll, N<:Real}

Project a set to a given block and set type with a certified error bound.

Input

• S – set
• block – block structure - a vector with the dimensions of interest
• set_type_and_precision – pair (T, ε) of a target set type T and an error bound ε for approximation
• n – (optional, default: dim(S)) ambient dimension of the set S

Output

A set representing the epsilon-close approximation of the projection of S.

Notes

Currently we only support HPolygon as set type, which implies that the set must be two-dimensional.

Algorithm

1. Project the set S with M⋅S, where M is the identity matrix in the block

coordinates and zero otherwise.

1. Overapproximate the projected set with the given error bound ε.

project(S::LazySet, block::AbstractVector{Int}, ε::Real, [n]::Int=dim(S);
        [kwargs...])

Project a set to a given block and set type with a certified error bound.

Input

• S – set
• block – block structure - a vector with the dimensions of interest
• ε – error bound for approximation
• n – (optional, default: dim(S)) ambient dimension of the set S

Output

A set representing the epsilon-close approximation of the projection of S.

Algorithm

1. Project the set S with M⋅S, where M is the identity matrix in the block

coordinates and zero otherwise.

1. Overapproximate the projected set with the given error bound ε.

The target set type is chosen automatically.

rectify(X::LazySet, [concrete_intersection]::Bool=false)

Concrete rectification of a set.

Input

• X – set
• concrete_intersection – (optional, default: false) flag to compute concrete intersections for intermediate results

Output

A set corresponding to the rectification of X, which is in general a union of linear maps of intersections.

Algorithm

For each dimension in which X is both positive and negative, we split X into these two parts. Additionally we project the negative part to zero.

permute(X::LazySet, p::AbstractVector{Int})

Permute the dimensions of a set according to a given permutation vector.

Input

• X – set
• p – permutation vector

Output

A new set corresponding to X where the dimensions have been permuted according to p.

rationalize(::Type{T}, X::LazySet{<:AbstractFloat}, tol::Real)
    where {T<:Integer}

Approximate a set of floating-point numbers as a set whose entries are rationals of the given integer type.

Input

• T – (optional, default: Int) integer type to represent the rationals
• X – set which has floating-point components
• tol – (optional, default: eps(N)) tolerance of the result; each rationalized component will differ by no more than tol with respect to the floating-point value

Output

A set of the same base type of X where each numerical component is of type Rational{T}.

singleton_list(P::LazySet)

Return the vertices of a polytopic set as a list of singletons.

Input

• P – polytopic set

Output

A list of the vertices of P as Singletons.

Notes

This function relies on vertices_list, which raises an error if the set is not polytopic (e.g., unbounded).

constraints(X::LazySet)

Construct an iterator over the constraints of a polyhedral set.

Input

• X – polyhedral set

Output

An iterator over the constraints of X.

vertices(X::LazySet)

Construct an iterator over the vertices of a polytopic set.

Input

• X – polytopic set

Output

An iterator over the vertices of X.

delaunay(X::LazySet)

Compute the Delaunay triangulation of the given polytopic set.

Input

• X – polytopic set
• compute_triangles_3d – (optional; default: false) flag to compute the 2D triangulation of a 3D set

Output

A union of polytopes in vertex representation.

Notes

This implementation requires the package MiniQhull.jl, which uses the library Qhull.

The method works in arbitrary dimension and the requirement is that the list of vertices of X can be obtained.

chebyshev_center_radius(P::LazySet{N};
                        [backend]=default_polyhedra_backend(P),
                        [solver]=default_lp_solver_polyhedra(N; presolve=true)
                       ) where {N}

Compute a Chebyshev center and the corresponding radius of a polytopic set.

Input

• P – polytopic set
• backend – (optional; default: default_polyhedra_backend(P)) the backend for polyhedral computations
• solver – (optional; default: default_lp_solver_polyhedra(N; presolve=true)) the LP solver passed to Polyhedra

Output

The pair (c, r) where c is a Chebyshev center of P and r is the radius of the largest ball with center c enclosed by P.

Notes

The Chebyshev center is the center of a largest Euclidean ball enclosed by P. In general, the center of such a ball is not unique, but the radius is.

Algorithm

We call Polyhedra.chebyshevcenter.

scale(α::Real, X::LazySet)

Concrete scaling of a set.

Input

• α – scalar
• X – set

Output

A new set of the same type (if possible).

Notes

This fallback method calls scale! on a copy of X.

plot_recipe(X::LazySet, [ε])

Convert a compact convex set to a pair (x, y) of points for plotting.

Input

• X – compact convex set
• ε – approximation-error bound

Output

A pair (x, y) of points that can be plotted.

Notes

We do not support three-dimensional or higher-dimensional sets at the moment.

Algorithm

One-dimensional sets are converted to an Interval.

For two-dimensional sets, we first compute a polygonal overapproximation. The second argument, ε, corresponds to the error in Hausdorff distance between the overapproximating set and X. On the other hand, if you only want to produce a fast box-overapproximation of X, pass ε=Inf.

Finally, we use the plot recipe for the constructed set (interval or polygon).

low(X::ConvexSet{N}, i::Int) where {N}

Return the lower coordinate of a convex set in a given dimension.

Input

• X – convex set
• i – dimension of interest

Output

The lower coordinate of the set in the given dimension.

high(X::ConvexSet{N}, i::Int) where {N}

Return the higher coordinate of a convex set in a given dimension.

Input

• X – convex set
• i – dimension of interest

Output

The higher coordinate of the set in the given dimension.

an_element(P::AbstractPolyhedron{N};
           [solver]=default_lp_solver(N)) where {N}

Return some element of a polyhedron.

Input

• P – polyhedron
• solver – (optional, default: default_lp_solver(N)) LP solver

Output

An element of the polyhedron, or an error if the polyhedron is empty.

Algorithm

An element is obtained by solving a feasibility linear program.

an_element(U::Universe{N}) where {N}

Return some element of a universe.

Input

• U – universe

Output

The origin.

σ

Function to compute the support vector σ.

support_vector

Alias for the support vector σ.

ρ(d::AbstractVector, S::LazySet)

Evaluate the support function of a set in a given direction.

Input

• d – direction
• S – set

Output

The evaluation of the support function of the set S for the direction d.

support_function

Alias for the support function ρ.

==(X::LazySet, Y::LazySet)

Check whether two sets use exactly the same set representation.

Input

• X – set
• Y – set

Output

• true iff X is equal to Y.

Notes

The check is purely syntactic and the sets need to have the same base type. For instance, X::VPolytope == Y::HPolytope returns false even if X and Y represent the same polytope. However X::HPolytope{Int64} == Y::HPolytope{Float64} is a valid comparison.

Algorithm

We recursively compare the fields of X and Y until a mismatch is found.

Examples

julia> HalfSpace([1], 1) == HalfSpace([1], 1)
true

julia> HalfSpace([1], 1) == HalfSpace([1.0], 1.0)
true

julia> Ball1([0.0], 1.0) == Ball2([0.0], 1.0)
false

≈(X::LazySet, Y::LazySet)

Check whether two sets of the same type are approximately equal.

Input

• X – set
• Y – set of the same base type as X

Output

• true iff X is equal to Y.

Notes

The check is purely syntactic and the sets need to have the same base type. For instance, X::VPolytope ≈ Y::HPolytope returns false even if X and Y represent the same polytope. However X::HPolytope{Int64} ≈ Y::HPolytope{Float64} is a valid comparison.

Algorithm

We recursively compare the fields of X and Y until a mismatch is found.

Examples

julia> HalfSpace([1], 1) ≈ HalfSpace([1], 1)
true

julia> HalfSpace([1], 1) ≈ HalfSpace([1.00000001], 0.99999999)
true

julia> Ball1([0.0], 1.0) ≈ Ball2([0.0], 1.0)
false

copy(S::LazySet)

Return a copy of a set by copying its values recursively.

Input

• S – set

Output

A copy of S.

Notes

This function computes a copy of each field in S. See the documentation of ?copy for further details.

eltype(::Type{<:LazySet{N}}) where {N}

Return the numeric type (N) of the given set type.

Input

• T – set type

Output

The numeric type of T.

eltype(::LazySet{N}) where {N}

Return the numeric type (N) of the given set.

Input

• X – set

Output

The numeric type of X.

CompactSet

An alias for compact set types.

Notes

Most lazy operations are not captured by this alias because whether their result is compact or not depends on the argument(s).

NonCompactSet

An alias for non-compact set types.

Notes

Most lazy operations are not captured by this alias because whether their result is non-compact or not depends on the argument(s).